the complexities of interpreting reversible elevated serum...

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Review

The complexities of interpreting reversible elevated serum creatinine levels in drug

development: Does a correlation with inhibition of renal transporters exist?

Xiaoyan Chu, Kelly Bleasby, Grace Hoyee Chan, Irene Nunes, Raymond Evers

Department of Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck Sharp &

Dohme Corporation, Kenilworth, New Jersey, USA (XC, KB, GHC, RE); GRA Onc,

Immunology, Biologics & Devices, Merck Sharp & Dohme Corporation, Kenilworth, New

Jersey, USA (IN)

This article has not been copyedited and formatted. The final version may differ from this version.DMD Fast Forward. Published on January 29, 2016 as DOI: 10.1124/dmd.115.067694

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Running Title: Serum creatinine and inhibition of renal transporters

Corresponding Author:

Xiaoyan Chu, Ph.D.

Merck & Co, RY800-D211

126 East Lincoln Avenue

Rahway, NJ 07065, USA

Tel 732-594-0977; Fax 732-594-2382; e-mail [email protected]

Number of text pages: 24

Number of tables: 3

Number of figures: 3

Number of references: 116

Number of words in abstract: 242

Number of words in introduction: 815

Non-standard abbreviations:

Serum creatinine (SCr), glomerular filtration rate (GFR), creatinine renal clearance (CLcr),

organic cation transporter 2 (OCT2), multidrug and toxin extrusion protein 1 and -2K (MATE1

and MATE2K), organic anion transporter 2 (OAT2), drug-drug interaction (DDI), in vitro-in


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vivo correlation (IVIVC), maximum plasma concentration (Cmax), unbound maximum plasma

concentration (Cmax,u), acute kidney injury (AKI).


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Abstract

In humans, creatinine is formed by a multistep process in liver and muscle and eliminated via the

kidney by a combination of glomerular filtration and active transport. Based on current

evidence, creatinine can be taken up into renal proximal tubule cells by the basolaterally

localized organic cation transporter 2 (OCT2) and the organic anion transporter 2 (OAT2), and

effluxed into the urine by the apically localized multidrug and toxin extrusion protein 1

(MATE1) and MATE2K. Drug induced elevation of serum creatinine (SCr) and/or reduced

creatinine renal clearance (CLcr) is routinely used as a marker for acute kidney injury (AKI).

Interpretation of elevated SCr can be complex, because such increases can be reversible and

explained by inhibition of renal transporters involved in active secretion of creatinine or other

secondary factors such as diet and disease state. Distinction between these possibilities is

important from a drug development perspective as increases in SCr can result in the termination

of otherwise efficacious drug candidates. In this review, we discuss the challenges associated

with using creatinine as a marker for kidney damage. Furthermore, in order to evaluate whether

reversible changes in SCr can be predicted prospectively based on in vitro transporter inhibition

data, an in depth in vitro-in vivo correlation analysis was conducted for sixteen drugs with in

house and literature in vitro transporter inhibition data for OCT2, MATE1 and MATE2K, as well

as total and unbound maximum plasma concentration (Cmax and Cmax,u) data measured in the

clinic.


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Introduction

Serum creatinine (SCr), an endogenous cation produced mainly by muscle metabolism, is the

most widely used marker to assess renal injury (Tschuppert et al., 2007). Traditional monitoring

for nephrotoxicity relies upon SCr measurements (Waikar et al., 2012). Creatinine is primarily

filtered through the kidney through the glomeruli, but depending on a number of factors ~10-

40% is actively secreted by the proximal tubule cells through transporter-mediated active uptake

and efflux (Levey et al., 1988; Breyer and Qi, 2010). Therefore, alterations in glomerular

filtration rate (GFR) and/or proximal tubular secretion of creatinine can lead to increases in SCr

and decreases in the estimated creatinine clearance. Elevation of SCr often results in reduction

of drug dose (Arya et al., 2013; Arya et al., 2014) and may lead to discontinuation of the

development of potentially promising drug candidates. Therefore, it is critical to distinguish

clinically relevant increases in SCr due to renal toxicity from the non-pathologic increase in SCr

attributed to the inhibition of renal transporters. Mild to moderate and reversible elevation of

SCr and decrease in creatinine renal clearance (CLcr) has been reported, which can be attributed

to inhibition of creatinine transporters without affecting renal function per se (Arya et al., 2013;

Arya et al., 2014). This is supported by the clinical observation that several drugs such as

cobicistat (Lepist et al., 2014), pyrimethamine (Opravil et al., 1993), cimetidine (Dubb et al.,

1978), and trimethroprim (Berglund et al., 1975) lead to increased levels of SCr without

affecting kidney function. Such observations have also been reported for several recently

approved drugs, including crizotinib (Brosnan et al., 2014; Camidge et al., 2014) and

dolutegravir (Koteff et al., 2012). Understanding the mechanism of active secretion of SCr and

how drugs may interfere with this process is therefore important from both a drug development

and clinical practice perspective where SCr is used as a marker of kidney injury.


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Acute kidney injury (AKI) is a common condition that complicates up to 7% of all-hospital

admissions and 25% of intensive care unit admissions (Klevens et al., 2007; Vaidya et al., 2008;

Minejima et al., 2011). While progress has been made in understanding the pathophysiology of

AKI and in the clinical care of patients with AKI, mortality rates have remained unchanged at

50-70% over the past 50 years (Minejima et al., 2011). Despite routine monitoring of systemic

drug levels and renal function using traditional blood and urinary markers of kidney injury (e.g.

creatinine, blood urea nitrogen, tubular casts, urinary concentrating ability), 10-20% of patients

receiving aminoglycoside therapy, for instance, will develop AKI (Rybak et al., 1999). The lack

of sensitive and specific markers of AKI limits the ability for early detection and intervention in

drug-induced nephrotoxicity.

In the kidney, the elimination of drugs and endogenous compounds, such as creatinine, is the net

result of passive glomerular filtration and reabsorption, as well as transporter-mediated active

tubular secretion and/or reabsorption. The major transporters in human proximal tubule cells

that play a role in the uptake of drugs and endogenous compounds from blood into proximal

tubule cells are the organic cation transporter 2 (OCT2), and the organic anion transporters 1 and

3 (OAT1 and 3; Figure 1). In the apical membrane, major efflux transporters involved in the

excretion of drugs into the urine are the multidrug and toxin extrusion protein 1 (MATE1) and

MATE2K, and the multidrug-resistance protein MDR1 P-glycoprotein (P-gp). Inhibition of

these transporters may alter systemic and tissue exposure of drugs, metabolites, and endogenous

compounds, which may subsequently lead to clinically significant drug-drug interactions (DDIs).

This can be of concern from a drug efficacy or safety perspective (Giacomini et al., 2010;

Hillgren et al., 2013). Other transporters, such as the breast cancer resistance protein (BCRP),

are also expressed in the proximal tubule (Figure 1), but their clinical significance is less well


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defined (Giacomini et al., 2010; Giacomini and Huang, 2013; Hillgren et al., 2013). In general,

drug transporters are promiscuous in substrate recognition and in addition to the charge of drugs,

other factors, such as polar surface area, molecular weight, and number of hydrogen bond donors

and acceptors, contribute to substrate specificity (Chang et al., 2006). Excellent reviews on renal

transporters have been published previously and the reader is referred to these for further details

(Masereeuw and Russel, 2010; Morrissey et al., 2013).

In this review we provide: 1) An overview of the biosynthesis and disposition of creatinine in

humans; 2) The current knowledge of transporters involved in the active renal secretion of

creatinine; 3) A discussion on potential mechanisms that could result in increased levels of SCr;

4) A retrospective analysis to assess the correlation of elevation of SCr and inhibition of the

renal transporters OCT2, MATE1 and MATE2K, and a discussion on the challenges associated

with the identification of reliable biomarkers for AKI; and 5) A discussion on whether creatinine

is a predictive and sensitive biomarker for DDIs attributed to inhibition of OCT2 and MATEs.

Markers of renal function

GFR is generally accepted as the best index of renal function in health and disease (Levey et al.,

2015) and it can be accurately assessed by the measurement of the clearance of an exogenous

substance such as inulin, 99mTC-DPTA, 125I-i-othlamate, or 51Cr-EDTA (Korhonen, 2015).

However, as these methods are expensive and inconvenient for use in the clinical setting, GFR is

routinely estimated (eGFR) from the measurement of SCr, using a variety of equations such as

those recommended by the Modification of Diet in Renal Disease (MDRD) study (National


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Kidney, 2002), and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) (Levey

et al., 2009), which take into account the impact of age, gender, and race on SCr.

The accuracy of the GFR estimate relies heavily upon the laboratory measurement of SCr. The

inter-laboratory differences in the measurement of SCr have been widely documented (Miller et

al., 2005; Seronie-Vivien et al., 2005). Miller et al. compared 50 methods of creatinine

measurements in 5624 laboratories with an isotope-dilution mass spectrometry (IDMS) reference

method and reported large bias and discrepancies between the methods and laboratories (Miller

et al., 2005). For example, measurements ranged from 0.87-1.21 mg/dL for the 0.90 mg/dL

creatinine reference sample. To put this into context, using the MDRD equation, a 0.1 mg/dL

change in creatinine for a 60-year-old woman causes a 10% change in calculated GFR. More

recently, the introduction of calibration standards which can be traced to the “gold standard”

isotope-dilution mass spectrometry (IDMS) method has helped resolve these concerns

(Korhonen, 2015).

During the last decade, there has also been increasing interest in cystatin C as an additional

endogenous marker of renal function. Cystatin C, produced at a constant rate by human

nucleated cells, is freely filtered, not actively secreted, or dependent on muscle mass or diet

(Nyman et al., 2015). Equations combining serum cystatin C and creatinine have been proposed

to provide a more accurate estimate of GFR (Inker et al., 2012).

Biosynthesis and disposition of creatinine


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Creatinine is a product of the degradation of creatine, which is an organic nitrogenous compound

playing an important role in cellular energy metabolism. Creatine is derived from dietary

sources and de novo synthesis. As illustrated in Figure 2, the biosynthesis of creatine in humans

accounts for ~50% of the daily requirement and is a two-step process: first guanidinoacetate is

formed from arginine and glycine precursors, under the control of L-arginine-glycine

amidinotransferase (AGAT), followed by the guanidoacetate methyl transferase (GAMT)

catalyzed transfer of a methyl group from S-adenosyl-methionine to produce creatine. AGAT

and GAMT activities have been reported in many tissues. However, they are most highly

expressed in kidney and liver, respectively (Edison et al., 2007; Beard and Braissant, 2010).

Creatine synthesis is balanced with that of dietary intake through feedback inhibition of AGAT.

On a creatine free diet, this pathway is fully active. However, when creatine is ingested through

the diet, AGAT is partially repressed and guanidinoacetate synthesis, and thus subsequent

creatine synthesis, is reduced (Heymsfield et al., 1983). Once synthesized, creatine is released

into blood circulation where it is taken up into muscle and other tissues by the Na+-Cl- dependent

creatine transporter SLC6A8 (Verhoeven et al., 2005). The majority (98%) of the total body

creatine pool is found in skeletal muscle, with small amounts also found in brain, kidney, and

liver (Heymsfield et al., 1983). Approximately 1.7% of the total creatine pool (creatine and

phosphocreatine) dehydrates to creatinine per day (Edison et al., 2007) and permeates through

the cell plasma membrane into the blood circulation.

As a low-molecular-weight cation (MW=113), creatinine is eliminated solely by renal excretion

through a combination of glomerular filtration and tubular secretion, with minimal binding to

plasma proteins and metabolism. Glomerular filtration, the passive process of ultrafiltration of


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plasma from blood as it crosses the glomerular capillaries, accounts for the large majority of the

renal elimination of creatinine (Levey et al., 2015), whereas the secretory component is

estimated to be 10-20% of total creatinine elimination in some reports (Breyer and Qi, 2010) and

up to 40% in others, under normal conditions (Levey et al., 1988). Net tubular reabsorption of

creatinine is uncommon, but may occur in infants and the elderly (Musso et al., 2009). During

chronic renal failure, the proportion of creatinine excreted by glomerular filtration decreases and

the fraction undergoing tubular secretion may increase to 50-60%. In addition, under conditions

of greatly reduced GFR, up to 60% of the daily creatinine generated may be eliminated by extra

renal routes, such as degradation by intestinal microflora (Shemesh et al., 1985; Levey et al.,

1988).

Beyond renal injury or disease, several factors are known to impact the formation and

elimination of creatinine, including exercise, diet, emotional stress, age, fever and trauma, as

well as inhibition of the secretory component by drugs (as discussed below) (Heymsfield et al.,

1983; Levey et al., 1988). For example, creatinine excretion declines in the elderly and this is

likely the result of several factors, including reduced muscle mass, decreased dietary protein

consumption and the net tubular reabsorption of creatinine (Heymsfield et al., 1983; Musso et al.,

2009).

Mathematical concepts of renal clearance of creatinine

The renal clearance of creatinine is determined by its glomerular filtration, tubular secretion, and

reabsorption:

CLcr＝CLfiltration ＋CLsecretion – CLreabsorption Eq. 1


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Where CLfiltration, CLsecretion, CLreabsorption represents creatinine clearance by renal filtration, tubular

secretion, and reabsorption, respectively.

CLcr can be described by Eq.2 (Shitara et al., 2005).

CLcr＝ (1－FR)･(fu･GFR＋CLsecretion)

＝(1－FR)･(fu･GFR＋(QR･fu･CLser,int/ QR＋fu･CLser,int)) Eq.2

Where FR, fu, GFR, QR, CLser, int represents the fraction reabsorbed, protein unbound fraction in

the blood, glomerular filtration rate, renal blood flow rate, and intrinsic clearance of tubular

secretion, respectively.

As described below, tubular secretion of creatinine involves transporter-mediated active uptake

and efflux. Therefore, CLser,int is saturable and may be inhibited by drugs that are inhibitors of

these transporters. FR may be in part saturable (Shitara et al., 2005), but the mechanism(s)

contributing to reabsorption of creatinine, particularly, the role of transporters, are not well

understood.

Imamura et al (Imamura et al., 2011) established mechanistic models to describe the renal

elimination of creatinine. The model analysis suggested that active tubular secretion contributed

significantly to the renal elimination of creatinine (30-60%), whereas the significance of

reabsorption depended on the models used.

Transporters involved in active renal secretion of creatinine

Several drugs are reported to impact creatinine secretion, thereby causing transient increase in

SCr without altering GFR (Table 2 and see below). The current hypothesis is that these changes

are explained by the reversible inhibition of transporters involved in tubular secretion of


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creatinine (German et al., 2012). We summarize in the sections below the current knowledge of

the role of transporters in the uptake of creatinine into the proximal tubular cells of the kidney

and efflux into the urine.

Transporters involved in creatinine uptake in the kidney

Renal uptake of creatinine has been studied by members of the SLC22A family, such as the

organic cation transporters OCT2 and OCT3, and organic anion transporters OAT1, OAT2, and

OAT3. Comprehensive reviews on these organic cation and anion transporters can be found in

several publications (Jonker and Schinkel, 2004; Koepsell et al., 2007; Burckhardt, 2012; Nigam

et al., 2015).

Expression and function of OCT2, OCT3, OAT1, OAT2 and OAT3

OCT2 is a renal organic cation uptake transporter primarily localized in the basolateral

membrane of the whole segment of the renal proximal tubule cells. It plays a major role in renal

uptake of mostly cationic compounds, but also transports some anionic and zwitterionic

compounds (Jonker and Schinkel, 2004). On the contrary, OCT3 is recognized as an

extraneuronal monoamine transporter (Jonker and Schinkel, 2004). It is widely expressed in

many tissues, such as liver, kidney, skeletal muscle, placenta and heart, as well as in glial cells

and epithelial cells of the choroid plexus, and neurons. OCT3 transports a wide range of

monoamine neurotransmitters, hormones and steroids (Wu et al., 1998). OCT3 mRNA was

detected in human kidney cortex; however, its level was much lower compared to OCT2

(Motohashi et al., 2002). Therefore, at least based on mRNA analysis, the importance of OCT3

in transport of cationic compounds in kidney is much less compared to OCT2 (Motohashi et al.,

2002). Nevertheless, recent studies in Oct3 (-/-) knockout mice demonstrate that deletion of


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Oct3 has an impact on pharmacokinetic and pharmacological effects of its substrates, such as

metformin (Chen et al., 2015).

OAT1, OAT2, and OAT3 are renal organic anion uptake transporters located in the basolateral

membrane of proximal tubules (Motohashi et al., 2002). OAT1 and OAT3 have overlapping

substrate specificities, and are responsible for the uptake of many anionic drugs, such as

antibiotics, antivirals, diuretics, uricosurics, statins, ACE inhibitors and antineoplastic drugs

(Burckhardt, 2012). In contrast to OAT1 and OAT3, the role of OAT2 is less well characterized.

More studies have emerged in this decade focusing on OAT2 expression in human kidney as

well as its role in renal tubular handling of drugs (Cheng et al., 2012; Lepist et al., 2014; Shen et

al., 2015). OAT2 is expressed in the basolateral membrane of renal proximal tubule cells as well

as in the sinusoidal membrane of hepatocytes (Kobayashi et al., 2005; Cheng et al., 2012). One

group showed that OAT2 was localized in both basolateral and apical membranes of human and

cynomolgus monkey renal proximal tubules, but only in the apical membrane of rat proximal

tubules (Shen et al., 2015). These findings suggest species differences for OAT2/Oat2

localization and possibly a role in reabsorption of OAT2 in primates. Species differences in

OAT2/Oat2 localization make rodents a poor translatable model to predict effects in primates for

substrates of this transporter. OAT2 has many substrates in common with OAT1 and OAT3.

However, several antiviral drugs eliminated exclusively in the urine were preferentially

transported by OAT2 and not by OAT1 and OAT3 (Cheng et al., 2012). OAT1, OAT2, and

OAT3 mRNA are present in human kidney cortex, with highest mRNA level observed for

OAT3, and different mRNA levels for OAT1 and OAT2 in two separate reports (Motohashi et


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al., 2002; Cheng et al., 2012). However, it is not known whether these differences in mRNA

levels translate into different amount of transporter protein.

Transporters involved in creatinine uptake

Creatinine has been reported to be an in vitro substrate for OCT2 (Urakami et al., 2004; Imamura

et al., 2011; Ciarimboli et al., 2012; Lepist et al., 2014), with a Michaelis constant (Km) range

from 2 mM to 56 mM, suggesting low affinity transport. Based on the physiological

concentration of creatinine in plasma (30-85 μM), OCT2-mediated transport of creatinine will

not be saturable, which is especially important for patients with reduced GFR (Urakami et al.,

2004). In vivo studies using Oct1/2 double knockout mice showed the significance of Oct in

creatinine secretion; creatinine clearance and renal accumulation of exogenous creatinine were

35-fold and 23-fold lower in Oct1/2 knockout mice compared to wild type mice, respectively

(Ciarimboli et al., 2012). One group, however, questioned the role of Oct2 in creatinine

transport as they did not observe significant difference in creatinine secretion in Oct1/2 knockout

mice compared to control mice (Eisner et al., 2010). This discrepancy may be explained by the

use of ketamine by Eisner et al. which has the potential to interfere with creatinine secretion

(Ciarimboli et al., 2012). It should be noted that species differences may complicate the

translation of the contribution of OCT2/Oct2 in creatinine transport from rodents to humans. For

instance, in mouse, both Oct1 and Oct2 are expressed in kidney, while in humans, only OCT2 is

expressed in kidney whereas OCT1 is predominantly expressed in liver (Jonker and Schinkel,

2004). More direct evidence of creatinine as an OCT2 substrate came from genome-wide

association studies (GWAS) showing acute elevation of SCr (24% increase) in cancer patients

following treatment with cisplatin, a known substrate and inhibitor of OCT2. The effect of


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cisplatin on creatinine secretion is attributed primarily to competitive inhibition of OCT2

transport (Ciarimboli et al., 2012).

In a genetic association study, an intergenic single nucleotide polymorphism (SNP) (rs2504954;

T->G), located on chromosome 6 in the region between the OCT2 and OCT3 genes, was

significantly associated with higher SCr level (Ciarimboli et al., 2012). Another non-coding

SNP (rs2279463; T>C) in the OCT2 gene was associated with creatinine metabolism (Kottgen et

al., 2010). On the other hand, a coding SNP in OCT2 (rs316019; S270A) has been associated

with reduced cisplatin-induced nephrotoxicity (Filipski et al., 2009) and reduced renal

elimination of metformin (Wang et al., 2008), but not with altered SCr levels. An intronic OCT2

SNP (rs316009; G->A), a highly correlated polymorphism to rs316019, showed a strong

association with tubular creatinine secretion and end-stage renal disease (Reznichenko et al.,

2013). Taken together, both in vitro and in vivo data indicate a role of OCT2 in tubular secretion

of creatinine.

Creatinine has also been reported to be an in vitro substrate for OCT3 (Imamura et al., 2011;

Ciarimboli et al., 2012; Lepist et al., 2014). Similar to OCT2, OCT3 transports creatinine with a

Km in the mmolar range (~1.9 mM for OCT2 and ~1.3 mM for OCT3) (Lepist et al., 2014).

Clinically significant polymorphisms have been identified in OCT3. It is unknown, however,

whether these SNPs have an impact on creatinine secretion or not (Aoyama et al., 2006; Sakata

et al., 2010). Based on current evidence, OCT3 is likely less important than OCT2 for creatinine

uptake in kidney.


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Opposing the traditional view of organic cationic pathways as the sole mechanism of creatinine

secretion in kidney, creatinine has been reported to be an in vitro substrate for OAT2 (Ciarimboli

et al., 2012; Lepist et al., 2014; Shen et al., 2015). A comprehensive analysis to identify the

transporters for creatinine was performed, in which each transporter’s mRNA and function were

measured (Lepist et al., 2014). Creatinine showed somewhat higher affinity towards OAT2 (Km

= 986 μM), as compared to OCT2 and OCT3. Other groups also observed higher affinity

transport of creatinine by OAT2 compared to that by other transporters (Shen et al., 2015).

OAT2 might contribute to creatinine secretion, and possibly reabsorption in human renal

proximal tubules, but clinical data are needed to support this hypothesis.

The role of OAT3 in creatinine secretion is unclear. Contradictory findings were observed in

vitro in OAT3 transfected cell lines (Urakami et al., 2004), and kinetic data have not been

reported. The involvement of mouse Oat3 in creatinine secretion is also unclear. Vallon et al.

showed that creatinine was transported by mouse Oat3 using Xenopus laevis oocytes, and renal

creatinine clearance was significantly reduced in Oat3 (-/-) compared to wild-type mice (Vallon

et al., 2012). However, Ciarimboli et al. did not observe any creatinine uptake by mouse Oat3 in

a transfected cell line (Ciarimboli et al., 2012). The contribution of OAT3 to renal creatinine

uptake in human was estimated to be very low based on a relative activity factor evaluation

(Imamura et al., 2011). For OAT1, several reports showed that creatinine was not a substrate for

this transporter (Urakami et al., 2004; Imamura et al., 2011; Ciarimboli et al., 2012; Lepist et al.,

2014).

Transporters involved in creatinine efflux in the kidney


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Creatinine transport has been studied by members of the SLC47A family, such as MATE1 and

MATE2K, and SLC22A family members, such as organic cation and carnitine transporters

OCTN1 and OCTN2, and organic anion transporter OAT4.

Expression and function of MATE1, MATE2K, OCTN1, OCTN2 and OAT4

MATEs are proton/organic cation antiporters. MATE1 is highly expressed in the kidney, liver,

adrenal gland, skeletal muscle and several other tissues, while MATE2K is specifically

expressed in kidney (Masuda et al., 2006). Both MATE1 and MATE2K play a role in the renal

tubular secretion of cationic drugs and endogenous compounds in humans (Yonezawa and Inui,

2011). OCTN1 and OCTN2 are organic cation transporters expressed in many tissues. They are

localized at the brush border membrane of the proximal tubules in kidney and play a role in L-

carnitine tissue distribution and renal reabsorption (Wu et al., 1999; Tamai, 2013). OAT4 is also

located at the brush border membrane of proximal tubules and mediates the bidirectional

transport of urate and some organic anions, in a substrate dependent manner (Miyazaki et al.,

2005; Hagos et al., 2007).

Transporters involved in creatinine efflux

Creatinine has been reported to be a substrate for MATE1 and MATE2K (Tanihara et al., 2007).

While MATEs function as efflux transporters in vivo, MATEs are often evaluated as uptake

transporters by manipulating extracellular pH in vitro. In interpreting in vitro data for MATEs, it

is assumed that the intra and extracellular binding sites have an equal affinity for substrates and

inhibitors. In vitro studies suggest that MATE1 and MATE2K are involved in tubular secretion

of creatinine (Tanihara et al., 2007; Lepist et al., 2014; Shen et al., 2015). The uptake window of


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creatinine by MATEs at extracellular pH 8.4 was relatively low, and only ~ 2-3 fold higher in

MATE1 and 1.3-3 fold higher in MATE2K transfected cells compared to control cells (Tanihara

et al., 2007; Lepist et al., 2014; Shen et al., 2015). Intracellular acidification by pretreatment with

ammonium chloride enhanced the uptake of creatinine by MATE1 and MATE2K (Tanihara et

al., 2007). Kinetic analyses showed that creatinine has low affinity towards MATE1 and

MATE2K, with Km values of ~10 mM and ~21 mM, respectively (Shen et al., 2015). Orthologs

of human MATE1, but not MATE2K, have been identified in rats and mice (Yonezawa and Inui,

2011). When studying the nephrotoxicity of cisplatin, a significant increase in creatinine was

observed in cisplatin-treated Mate1 knockout mice compared to control mice. In addition, the

combination of pyrimethamine, a selective inhibitor of mouse Mate1, with cisplatin significantly

increased creatinine levels compared to cisplatin alone in wild type mice. Both studies indirectly

suggested a role of Mate1 in creatinine transport, at least in mice (Nakamura et al., 2010).

Several polymorphisms have been identified in MATE1 (rs111060524-G64D, rs111060526-

A310V, rs111060527-D328A, rs111060528-N474S), and MATE2K (rs111060529-K64N and

rs111060532-G211V) in Japanese subjects, and these variants were associated with loss of

transport activity of TEA and metformin in vitro (Kajiwara et al., 2009). Other MATE1 SNPs

(rs35646404 -T159M and rs35790011-V338I) have also been described with similar reduction in

transport activity of TEA and metformin in other subjects from various ethnic groups (Meyer zu

Schwabedissen et al., 2010). The effect of these MATE1 and MATE2K variants on creatinine

transport remains to be elucidated.


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To date, no reports have demonstrated creatinine transport by OCTN1 and/or OCTN2. It is

unclear whether creatinine is reabsorbed by proximal tubular cells through OAT4, as OAT4

functions as a bidirectional transporter (Hagos et al., 2007), suggesting that it could be involved

in excreting substrates into urine and/or reuptake of substrates from urine into cells. Using

different transfected cell lines, some observed creatinine uptake by OAT4 (Imamura et al.,

2011), but this was not confirmed by others (Lepist et al., 2014).

In summary, based on current evidence, OCT2, MATE1 and MATE2K are the major

transporters involved in renal creatinine secretion. OAT2 also could be involved based on in

vitro evidence, but its in vivo relevance in humans is not clear yet.

Inhibition of renal transporters and elevation of SCr: an IVIVC analysis

In drug development, it is desirable to develop approaches to understand underlying mechanisms

for interactions of drug candidates with active renal secretion of creatinine and to subsequently

distinguish clinically relevant increases in SCr due to impairment of renal function from non-

pathologic increases in SCr caused by inhibition of renal transporters. We therefore conducted a

retrospective analysis to evaluate whether an in vitro-in vivo correlation (IVIVC) exists between

inhibition of the renal transporters OCT2, MATE1, MATE2K, OAT2, and OCT3 and elevations

of SCr and /or decreases in CLcr. In this analysis (Tables 1 and 2), a total of 16 compounds were

identified that showed: 1) ≥10% reversible elevation of SCr without a significant change of

measured GFR (cimetidine, pyrimethamine, trimethoprim, dronedarone, DX-619, dolutegravir,

cobicistat, ritonavir, ranolazine, rilpivirine, and telaprevir); 2) >10% reversible elevation of SCr,

without reported data on changes in GFR (amiodarone, vandetanib); and 3) No significant

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negative in vivo controls (famotidine, ranitidine, and raltegravir). In vitro inhibition data (IC50

or Ki values) for human OCT2, MATE1, MATE2K, OAT2, and OCT3 were collected for these

compounds from the University of Washington DDI database

(https://www.druginteractioninfo.org). The range of IC50 or Ki values is summarized in Table 1.

To better understand the correlation between in vitro inhibition of OCT2 and MATEs, and the

elevation of SCr, in vitro IC50 values for inhibition of OCT2, MATE1 and MATE2K for 15

compounds listed in Table 1 were measured at Merck Research Laboratories using metformin as

the probe substrate and the method described by Rizk et al. in CHO-K1-OCT2, CHO-K1-

MATE1, and MDCKII-MATE2K cells (Rizk et al., 2013). Although creatinine is an ideal in

vitro probe for IVIVC evaluations, its assay window in OCT2 and MATEs uptake assays is

relatively low (our unpublished observations) (Lepist et al., 2014; Shen et al., 2015), and

therefore it is unsuitable for measuring IC50 values.

IVIVC analysis in this review will be focused on OCT2 and MATEs. In vitro inhibition data for

OAT2 and OCT3, which were recently identified as renal creatinine transporters, are currently

available only for a few compounds (Table 1). These compounds generally show weak

inhibition of OAT2 and OCT3 compared to MATEs and/or OCT2, suggesting that inhibition of

these transporters might be clinically less relevant. Indomethacin is a relatively potent in vitro

inhibitor of OAT2 (IC50 = 2.1 µM) (Shen et al., 2015). However, the effect of indomethacin on

elevation of SCr in several clinical studies is controversial (Prescott et al., 1990; Al-Waili, 2002).

In female healthy volunteers, indomethacin (150 mg daily for 3 days) had no significant effect on

SCr, GFR, or renal blood flow (Prescott et al., 1990). However, indomethacin was reported to

increase SCr in neonates (Al-Waili, 2002). As indomethacin is a potent prostaglandin synthesis


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inhibitor, it is likely that mechanisms other than transporter inhibition could result in the

observed elevation of blood creatinine (Al-Waili, 2002).

In vitro inhibition data with OCT2, MATE1 and MATE2K in the literature showed high

variability for several compounds (Table 1). For instance, the in vitro IC50 or Ki for ritonavir

with MATE1 showed a 193-fold variability, and inhibition of OCT2 by trimethoprim and

cimetidine showed a 101- and 99-fold variability, respectively. The reasons for this high

variability are not understood, but could be caused by the use of different probe substrates, and

differences in in vitro systems and assay conditions. For example, remarkable substrate-

dependent difference in IC50 values for inhibition of MATE2K by trimethroprim were reported

(47-fold, metformin vs. N-methylnicotinamide as probes) (Muller et al., 2015), and for OCT2

inhibition by vandetanib (13-fold, MPP+ vs. metformin as probes) (Shen et al., 2013) when the

studies were conducted in the same laboratory using the same in vitro system. Substrate-

dependent inhibition of OCT2, MATE1, and MATE2K has been systematically systemically

evaluated with several prototypic substrates (Belzer et al., 2013; Martinez-Guerrero and Wright,

2013), suggesting that both OCT2 and MATEs have multiple drug binding sites. In contrast to

such substrate dependent inhibition, several other studies have shown consistent Ki or IC50

values with selected OCT2/MATEs inhibitors across different probe substrates. For instance, Ito

et al. reported no markedly substrate dependence in cimetidine Ki values for OCT2, MATE1, and

MATE2K with five probe substrates (Ito et al., 2012b). Likewise, similar IC50 values were

obtained with cobicistat for OCT2 and MATE1 using TEA and creatinine as probe substrates

(Lepist et al., 2014). Nevertheless, development of predictive DDI models for OCT2 and

MATEs need to take into account the potential for substrate dependence of ligand interactions

with these proteins. Furthermore, different in vitro systems and assay conditions may have a


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marked effect on IC50 variability. For example, in studies where metformin was used as probe

substrate, ritonavir IC50 for MATE1 was 0.08 µM when pre-incubating MATE1 transfected HEK

293 cells for 30 min in a 30 mM NH4Cl buffer to create an artificial pH gradient (Wittwer et al.,

2013), whereas the IC50 was 15.4 µM when using MATE1 transfected HeLa cells without pre-

incubation with NH4Cl (Meyer zu Schwabedissen et al., 2010).

In Table 2, the risk for in vivo inhibition of OCT2, MATE1 and MATE2K was assessed by

comparing total and unbound maximal plasma concentrations (Cmax and Cmax,u) of test

compounds with in vitro IC50 values (Cmax/IC50 and Cmax, u/IC50). A cut-off of Cmax/IC50 ≥ 0.1

and Cmax,u/IC50 ≥ 0.1 was used to predict the risk for in vivo inhibition of respective transporters.

As the relative contribution of these transporters (fraction transported) and the rate-determining

step for renal secretion of creatinine are not well known, we assume that OCT2, MATE1, and

MATE2K are contributing equally to the renal secretion of creatinine. Therefore, in assessing

the existence of an IVIVC, inhibition of any of the above transporters was considered as an

indication of in vivo inhibition of creatinine secretion as the worst case scenario. As shown in

Table 2, using our in house IC50 data, Cmax/IC50 (≥ 0.1) provided a reasonably good prediction for

the elevation of SCr for this set of compounds as there were no false negative predictions. Use

of Cmax,u/IC50 (≥ 0.1) resulted in four false negatives (dronedarone, cobicistat, rilpivirine, and

telaprevir). Both Cmax/IC50 and Cmax,u/IC50 resulted in a false positive prediction for famotidine

(40mg QD for 7days) and ranitidine.

Considering the variability of IC50 and Ki values reported in the literature, using lowest IC50 or

Ki values for OCT2, MATE1, and MATE2K available for 11 compounds (Table 1), Cmax/IC50 (≥

0.1) provided a reasonably good prediction for the elevation of SCr, whereas Cmax,u/IC50 (≥ 0.1)

resulted in a false negative prediction for cobicistat (data not shown). Likewise, using the


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highest IC50 or Ki values reported for OCT2, MATE1, and MATE2K, Cmax/IC50 (≥ 0.1) still

provided a good prediction of the elevation of SCr, whereas Cmax,u/IC50 (≥ 0.1) resulted in false

negative prediction for cobicistat, dolutegravir, ritonavir, and vandetanib. Use of either the

lowest or highest IC50 literature values, Cmax/IC50 and Cmax,u/IC50 both resulted in false positive

predictions for famotidine (40mg QD for 7days) and ranitidine (data not shown). However,

Hibma et al. (Hibma et al., 2015) have recently reported an elevation of SCr and a reduction in

CLcr by famotidine in humans at a single dose of 200 mg and multiple doses of 160 mg, which

were 4-5 fold higher than in a previous report (Ishigami et al., 1989) (Table 2). The reason for

the lack of IVIVC for these two compounds at clinically relevant exposure is unclear. As there

are no major circulating metabolites for ranitidine and famotidine, it is less likely for metabolites

to cause transporter inhibition. An effect on reabsorption of creatinine cannot be excluded,

however.

Currently, Cmax,u/IC50 ≥ 0.1 is being recommended by the FDA for OCT2 (CDER, 2012.) and the

International Transporter Consortium (ITC) for OCT2 and MATEs (Hillgren et al., 2013) as the

cut-off value to assess the risk for DDIs with OCT2/MATEs transporters. For prediction of

transporter related DDIs, it is critical to use relevant inhibitor concentrations, which are unbound

inhibitor concentrations at the site of interactions with the transporter of interest. As such, Cmax,u

will be the relevant concentration for predicting DDI with OCT2, which is localized in the

basolateral plasma membrane of renal proximal tubule cells, whereas it may not be adequate to

predict DDIs for efflux transporters, such as MATEs, as these are localized in the apical plasma

membrane. For example, if the inhibitor is actively taken up by the proximal tubule cells, Cmax,u

may under-estimate the inhibitory effects for efflux transporters. Thus, unbound intracellular

inhibitor concentrations in relevant tissues would be more relevant for prediction of efflux


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transporter related DDIs. However, the methodologies to measure and/or predict such values are

currently still limited (Chu et al., 2013).

Is creatinine a sensitive biomarker for renal cationic transporter-related DDIs?

Determining the impact of perpetrator drugs on plasma concentration or urinary excretion of

suitable endogenous biomarkers is a valuable tool to assess the risk for drug interactions early in

drug development (e.g. Phase I clinical trials). Recently, some endogenous probes for studying

renal cationic transporter related DDIs have been identified. Ito et al. have found that the

endogenous metabolite N-methylnicotimide (NMN), a substrate for OCT2, MATE1 and

MATE2K, could be used as an endogenous probe to study the DDIs related to OCT2/MATEs

inhibition in humans (Ito et al., 2012a). Pyrimethamine, a potent inhibitor of MATE1 and

MATE2K near completely diminished tubular secretion of NMN (renal clearance 403 vs. 119

ml/min), but had minimal effect on plasma exposure of NMN. Furthermore, Muller et al.

(Muller et al., 2015) reported that trimethoprim, another OCT2/MATEs inhibitor, decreased

NMN renal clearance by 19.9% without significant impact on NMN plasma AUC. The

magnitude of trimethoprim-induced renal clearance reduction was positively correlated between

NMN and metformin in 12 subjects, suggesting the potential use of NMN as endogenous probe

for DDIs involving OCT2/MATEs. Using untargeted metabolomics analysis of urine specimens

from healthy subjects and mice treated with or without pyrimethamine, Kato et al. (Kato et al.,

2014) found that thiamine, a vitamin B1, which is essential for carbohydrate metabolism and

neural function, is also a potential biomarker for inhibition of MATE1 and MATE2K.

To evaluate if creatinine can be used as a biomarker to assess OCT2/MATEs related DDIs, we

searched the literature for examples where clinical DDIs can be mechanistically explained by


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inhibition of OCT2 and/or MATEs, and the changes in SCr or CLcr were measured in the same

clinical studies. As shown in Table 3, observed DDIs with several OCT2/MATEs inhibitors

(cimetidine, pyrimethamine, trimethoprim, and vandetanib) at the dose indicated correlated with

a 10-30% elevation in SCr or a decrease in CLcr. This was not the case for ranitidine, however,

as it caused DDIs with procainamide and triamterene without affecting SCr levels. Interestingly,

famotidine, a recently reported MATE1 selective inhibitor, significantly increased SCr (200mg

QD and 160mg q4hr) without affecting the plasma exposure of metformin (Hibma et al., 2015).

The latter likely is explained by the opposing effects of the famotidine-induced increase in both

metformin absorption and renal clearance. Elevation of SCr by cimetidine was variable and less

sensitive in some DDI studies at the clinically relevant dose of 300-400 mg. Considering the

weak to moderate change of SCr associated with OCT/MATEs related DDIs and that a range of

other factors may potentially impact SCr exposure, as we have discussed elsewhere in this

review, SCr does not appear to be a biomarker with sufficient sensitivity to assess the risk, either

qualitatively or quantitatively, of inhibition of OCT2 or MATEs in humans. Follow-up

mechanistic studies such as transporter inhibition experiments are still useful, however, in cases

where increases in SCr exposure are observed.

Is serum creatinine an appropriate marker for renal injury?

Traditional monitoring for nephrotoxicity relies upon the measurement of SCr. However, SCr

retains poor specificity for AKI and is insensitive to the degree of AKI for three reasons. First, a

large amount of nephron loss can occur without significant changes in SCr due to residual renal

reserve. This fact is most clearly evident in kidney donors in whom no significant change in SCr


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occurs despite a loss of 50% of functioning renal mass (Bosch et al., 1983). Second, the rate of

rise in SCr following a renal insult is delayed due to the kinetics of creatinine production from

muscle turnover and accumulation secondary to reduced glomerular filtration. At a normal GFR

of 120 mL/min, the serum half-life of creatinine is approximately 4 hours; however, at a GFR of

30 mL/min the half-life extends to 16 hours and will therefore not reach steady state for nearly 3

days (Waikar and Bonventre, 2009). Third, as previously discussed, SCr is influenced by a

number of other factors including inhibition of tubular secretion by drugs, weight, gender, age,

muscle metabolism, hydration state, and protein intake (Blantz, 1998). Reduced muscle mass

secondary to malnutrition or immobility is a frequently observed clinical problem that severely

limits the utility of SCr as a marker of kidney function. Based on the limitations of SCr, there

has been great interest in the identification of alternate markers of renal function. To date, a

number of promising biomarker candidates have been identified, characterized, and validated

using models of kidney injury in animals or described for various clinical settings in humans

such as sepsis, cardiac bypass surgery, and contrast media exposure (Fuchs and Hewitt, 2011;

Waring and Moonie, 2011; Vanmassenhove et al., 2013). Importantly, the utility of these new

biomarkers in detecting drug-induced AKI clinically in either the patient-care or drug

development setting has not been established. Presently, urine biomarkers have been agreed by

regulatory agencies to be used for nonclinical phases of drug development, and on a case-by-case

basis for clinical drug development research investigation (Dieterle et al., 2010). Clinical

qualification of novel AKI urine biomarkers for use during clinical drug development is

currently on-going.

Conclusions


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Based on the in vitro and pharmacogenomic evidence available, OCT2 is one of the transporters

involved in the uptake of creatinine into kidney proximal tubule cells, but its quantitative

involvement is unknown. More recent in vitro data suggests that OAT2 also transports

creatinine efficiently, but to what extent this is relevant in humans is not yet clear. Following

uptake into the kidney, MATE1 and MATE2K mediate the efflux of creatinine into the urine.

Important questions that remain are whether uptake or efflux is rate-determining in the active

secretion of creatinine, what the relative contribution is of each transporter in this process, and

whether there are yet unidentified transporters involved in creatinine excretion and/or

reabsorption. Similar to hepatobiliary transport, it is generally hypothesized that uptake is the

rate-limiting step for active tubular secretion, if the luminal efflux is markedly greater than the

basolateral efflux. In this case, the inhibition of the luminal efflux should have less impact on

the overall systemic intrinsic clearance. However, this cannot explain the significant elevation of

SCr by pyrimethamine, a selective inhibitor of MATEs relative to OCT2.

Currently, the effect of drugs on creatinine transport is measured in cell lines transfected with

individual transporters. Recently, a quintuple in vitro transporter model expressing

OAT2/OCT2/OCT3/MATE1/MATE2K has been explored to evaluate the impact of test

compounds on creatinine transport (Zhang et al., 2015), but more data are needed to establish the

predictive value of this model. Development and use of holistic models and integrated systems,

for instance, immortalized cell lines derived from human kidney with preserved activity of

transporters and drug metabolizing enzymes, may provide more physiologically relevant models

to study the interaction of drugs with the renal secretion of creatinine in the future (Schophuizen

et al., 2015).


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Overall, high variability of in vitro transporter inhibition data, not limited to OCT2/MATEs, has

become a significant concern and may limit IVIVE using universal cut-off values for transporter

perpetrator decision trees which trigger clinical DDI studies (Bentz et al., 2013). Although the

underlying mechanisms for IC50 variability can be complex, proper standardization of in vitro

inhibition assays by, for example, the use of clinically relevant probe substrates, and

standardized incubation conditions, and cell lines will be helpful for improving IVIVE.

In an attempt to establish an IVIVC between inhibition of OCT2, MATE1 and MATE2K, several

false negatives were identified using a cut-off for the ratio of Cmax,u/IC50 or Cmax,u/ Ki of ≥ 0.1.

The true positive rate was higher if total (bound plus unbound) drug concentrations were used for

the analyses. Since only unbound drug will be available for interactions with transporters, this

suggests that the free drug concentration measured in plasma is lower than in the proximal tubule

cells or that mechanisms other than inhibition of MATEs and OCT2 contribute to the effects on

creatinine. For example, although cobicistat is an inhibitor of MATE1 in vitro, this inhibition is

not predicted to be clinically significant based on Cmax,u/IC50 data. Remarkably, famotidine and

ranitidine were identified as inhibitors of MATEs and OCT2-mediated creatinine transport in

vitro, whereas no effect on creatinine was observed at clinically relevant exposures. Currently,

we have no good explanations for the lack of IVIVC for these compounds. In the future, use of

mechanistic models may improve the prediction of in vivo interaction of drug molecules with

creatinine renal transporters.

Due to potential interactions of drug molecules with creatinine secretion along with several other

limitations, an alternative method to estimate GFR would be desirable. Despite ongoing efforts

to identify more sensitive and specific markers for renal function and injury, currently, use of

creatinine to estimate GFR is still a practical approach. As such, if a transient and /or reversible


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elevation of SCr was observed during drug development, understanding the potential interaction

of drug molecules with active renal secretion of creatinine and carefully monitoring renal

function with alternate markers such as cystatin C would be recommended.

Since organic cation transporters such as OCT2 and MATEs are known to transport endogenous

compounds (Jonker and Schinkel, 2004), it would be valuable from a drug development

perspective if changes in these compounds could be used as biomarkers for assessing DDIs

involving inhibition of these transporters. In the case of the kidney, excretion of such

biomarkers would need to be excreted to a significant extent by active transport (as opposed to

GFR), levels should not be affected by secondary factors such as diet and disease, not be

sensitive to diurnal effects, and would need to be selective for the transporter(s) of interest.

Based on these criteria and our retrospective analysis of in vitro and clinical data, creatinine is

not an optimal biomarker as its synthesis involves multiple steps, external factors such as diet

and exercise affect plasma levels, and the contribution of active transport to clearance is

relatively small and not consistent between patient populations. However, mechanistic studies

to explain increases in creatinine in the absence of a decrease in GFR will continue to be

important.


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Acknowledgement

The authors thank Dr. Lisa A. Shipley for continuous support; Robert Houle for technical

assistance; and Drs Kathleen Cox, Nancy G. B. Agrawal, Sevgi Gurkan for valuable comments

on the manuscript.


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Authorship Contributions

Participated in research design: N/A

Contributed new reagents: N/A

Conducted experiments: Chan

Performed data analysis: Chu, Bleasby, Chan, Evers

Wrote or contributed to the writing of the manuscript: Chu, Bleasby, Chan, Nunes, Evers


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Legends for Figures

Figure 1: Membrane transporters expressed on human renal proximal tubule cells

The transporters located in the basolateral plasma membrane include organic anion transporter 1

(OAT1; SLC22A6), OAT3 (SLC22A8), OAT2 (SLC22A7), organic cation transporter 2 (OCT2;

SLC22A2), OCT3 (SLC22A3), and organic anion transporting polypeptide 4C1 (OATP4C1;

SLCO4C1). Transporters located in the apical membrane include P-glycoprotein (P-gp; MDR1,

ABCB1), multidrug and toxin extrusion protein 1 (MATE1; SLC47A1), MATE2K (SLC47A2),

breast cancer resistance protein (BCRP; ABCG2), multidrug resistance protein 2 (MRP2;

ABCC2), MRP4 (ABCC4); OAT4 (SLC22A11), urate transporter 1 (URAT1; SCL22A12),

peptide transporter 1 (PEPT1; SLC15A1) and PEPT2 (SLC15A2), organic cation/carnitine

transporter 1 (OCTN1; SLC22A4) and OCTN2 (SLC22A5).

Figure 2: Schematic representation of the biosynthesis and disposition of creatine and

creatinine.

Figure 3: Schematic representation of renal elimination of creatinine and the transporters

known to transport creatinine in vitro


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Table 1. In vitro inhibition (IC50 or Ki) of selected compounds on human OCT2, MATE1, MATE2K, OAT2, and OCT3

Inhibitors OCT2 IC50 or Ki (µM) MATE1 IC50 or Ki (µM) MATE2K IC50 or Ki (µM) OAT2 IC50

or Ki (µM)

OCT3 IC50

or Ki (µM)

Probe

Metformin

(10 µM)a

Reported

in the

literatureb

Probe

Metformin

(5 µM)a

Reported

in the

literatureb

Probe

Metformin

(5 µM)a

Reported

in the

literatureb

Reported

in the

literatureb

Reported

in the

literatureb

Cimetidine 2.9 ± 0.7 16.6-1650 0.6 ± 0.05 0.2-16.3 5.6±0.7 2.1-46.6 22-72.8 9.8-111

Pyrimethamine 0.61 ± 0.04 4.8-23.6 0.02 ±

0.002

0.077-0.63 0.045±0.003 0.046-

0.52

- -

Famotidine 21.6 ± 3.4 36.1-1800 0.45 ± 0.03 0.6-0.76 6.6±0.9 9.7-36.2 - 6.7-14

Ranitidine 11.7 ± 1.3 30.5-79 8.2 ± 0.6 8.3-25.4 21±2 25 - 62-290

Trimethoprim 19.8±1.5 13.2-1327 0.51±0.03 3.31-29.1 0.14±0.02 0.61-28.9 NI 12.3

Dronedarone 1.9±0.3 - 0.46±0.02 - 8.9±1.7 - - -

DX619 - 0.94-1.29 - 0.82-4.32 - 0.1 - -

Dolutegravir 0.21±0.04 0.066-1.93 3.6±0.7 4.67 12.5±1.6 >100 >100 >100

Cobicistat 37.9±4.9 8.24-33 0.98±0.19 0.99-1.87 20.5±3.0 33.5 >100 >100

Ritonavir 24.8±3.4 20-25 0.28±0.05 0.08-15.4 40.1±6.5 23.7 >20 300

Ranolazine 47 ± 9 - 16.8±1.5 - 50±8 - - -

Rilpivirine 0.38±0.05 5.13 0.25±0.04 - 0.28±0.08 - - -

Amiodarone 4.7±1.1 >1000 1.0±0.2 - >50 - - >1000

Raltegravir >100 >100 >100 >100 >100 >100 - -

Telaprevir >100 6.35 62±5 22.98 >100 - - -

Vandetanib 0.4±0.05 5.5-73.4 0.06 ± 0.01 0.16-1.23 0. 04 ± 0.01 0.3-1.26 - -

-: Data are not reported or available. a: Data generated at Merck & Co for inhibition of OCT2, MATE1 and MATE2K using metformin as the probe

substrate in CHO-K1-OCT2, CHO-K1-MATE1, and MDCKII-MATE2K cells using the method described by Rizk et al (Rizk et al., 2013). b: Data

obtained from the University of Washington DDI database (https://www.druginteractioninfo.org).

This article has not been copyedited and form

atted. The final version m

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Table 2. Effect of selected compounds on SCr, CLcr, and GFR in humans and the correlation with in vitro inhibition of OCT2, MATE1

and MATE2K

Inhibitors Dosing

regimen

SCr

↑(%)

CLcr↓(

%)

GFR

↓

Markers

for GFR

Cmax

(uM)

fu Cmax/IC50a C max,u/IC50

a References

OCT

2

MATE1 MATE

2K

OCT2 MATE1 MATE

2K

Cimetidine 400 mg

(QDS)

13-26 20 NS 51

Cr-

EDTA;

inulin

8-12 0.8 4.14 20.00 2.14 3.31 16.00 1.71 (Hilbrands et

al., 1991)

(Dutt et al.,

1981)

Pyrimethamine 50-100mg

SD

18-26

-

25-27

NS inulin 2.3a 0.13 3.77 115.00 51.11 0.49 14.95 6.64 (Kusuhara et

al., 2011)

(Opravil et

al., 1993)

Famotidine 40mg QD,

7days

NS NS - - 0.39 0.8 0.02 0.87 0.06 0.01 0.69 0.05 (Ishigami et

al., 1989)

Famotidine 200mg SD;

160mg q4h

SI SI - - 1.25 0.8 0.06 2.78 0.19 0.05 2.22 0.15 (Hibma et

al., 2015)

Ranitidine 300mg QD NS NS - - 3.72 0.85 0.32 0.45 0.18 0.27 0.39 0.15 (Motyl,

2004)

Trimethoprim 20mg/kg/d

ay (10

days);

200mg BID

31 16 NS 51

Cr-EDTA

iothalama

te

3.4-

6.9

0.56 0.35 13.53 49.29 0.20 7.58 27.60 (Naderer et

al., 1997;

Arya et al.,

2014)

Dronedarone 400mg bid

7days

10-15 18 NS Sinistrin

PAH

0.30 0.02 0.16 0.65 0.03 0.003 0.013 0.001 (Tschuppert

et al., 2007)

DX-619 800mg (qd)

[4 days]

30-40 26 NS iohexol 20.5-

22

0.29-

0.35b

23.4

0

26.83 220.0 8.19 9.39 77.0 (Sarapa et

al., 2007)

Dolutegravir 50mg (qd

or bid, 14

days)

9-17

10 -14

NS Iohexol

Cystatin C

6.7-

13.1

0.01 62.3

8

3.64 1.05 0.62 0.04 0.01 (Koteff et al.,

2012)

Cobicistat 150 mg

QD,

7 d, p.o.

10.5,

23

8 – 14,

9 - 20

NS Iohexol 1.55 0.03 0.04 1.58 0.08 0.001 0.05 0.002 (Cohen et

al., 2011;

German et

al., 2012);

(Arya et al.,




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2014)

Ritonavir 100mg QD - 25 NS Iohexol 2.16 0.015 0.09 7.71 0.05 0.001 0.12 0.001 (Deray et al.,

1998;

German et

al., 2012;

Lepist et al.,

2014)

Ranolazine 1000 mg

BID, 5 d,

p.o.

12 10 NS Sinistrin 6.01 0.38 0.13 0.36 0.12 0.05 0.14 0.05 (Arya et al.,

2014)

Rilpivirine 25mg (qd,

96 weeks)

10 - NS Cystatin C 0.6 0.005 1.58 2.40 2.14 0.01 0.01 0.01 Drug label;

(Maggi et al.,

2014)

Amiodarone 400mg QD 11 - - - 0.8-

2.3

0.04 0.49 2.30 0.05 0.02 0.09 0.002 (Pollak et al.,

1993)

Raltegravir 400mg BID NS NS - - 3.38 0.17 <0.0

3

<0.03 <0.03 <0.01 <0.01 <0.01 Drug label;

(Rizk et al.,

2013; Maggi

et al., 2014)

Telaprevir 750mg q8h SI - NS Cystatin C,

L-FABP,

NAG

5.82 0.04-

0.24 b

<0.0

6

0.09 <0.06 0.01 0.02 <0.01 (Suzuki et

al., 2013;

Matsui et

al., 2015)

Vandetanib 300mg

QD

15 - - - 0.33 0.1 0.83 5.50 8.25 0.08 0.55 0.83 (Shen et al.,

2013)

-: Data are not reported or available. NS: Not significant (either statistically or clinically). SI: Significantly increased compared to baseline level; a: IC50

used are generated at Merck & Co and shown in Table 1, except for DX-619, for which lowest IC50 values obtained from the literature are used

(see Table 1). b: highest fu values are used to estimate C max, u/IC50 as the worst case scenario. Data generated at Merck & Co 50mg oral SD




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Table 3. Examples of transporter related DDIs involving OCT2, MATE1, and/or MATE2K and correlation with transient elevation of

sCr

Perpetrator Perpetrator dose regimen Victim Victim dose regimen % Change

in AUC

% Change in

renal CL

Elevation

of SCr

and/or %

decrease

of CLcr

References

cimetidine 400 mg QID [8 days] gabapentin 1200 mg QD [4 days] 23.7 -17.8 Yes;

CLcr↓10%

(Lal et al.,

2010)

cimetidine 800 mg BID [6 days] glycopyrronium 100 ug Inhalation SD 19.2 -22.1 - (Dumitras et

al., 2013)

cimetidine 400 mg BID [6.5 days] metformin 500 mg SD 54.2 -44.6 No; CLcr

NS

(Wang et al.,

2008)

cimetidine 400 mg BID [5 days] metformin 250 mg QD [10 days] 46.2 -28.3 Noa (Somogyi et

al., 1987)

cimetidine 300 mg TID [5 days] varenicline 2 mg SD 29.7 -25.1 Yes ;

CLcr↓5-

10%

(Feng et al.,

2008)

dolutegravir 50 mg BID [7 days] metformin 500 mg BID [12 days]

145 - Yes; SCr ↑ (Zong et al.,

2014)

pyrimethamine 50 mg SD metformin 250 mg SD 35.3 -35 Yes;

CLcr↓20%

(Kusuhara et

al., 2011)

ranitidine 150 mg BID procainamide 1 g SD 13.7 -18.5 - (Somogyi and

Bochner,

1984)

ranitidine 150 mg BID [4 days] triamterene 100 mg/day QD [8

days]

-24 -51 No; NS

Clcr

(Muirhead et

al., 1988)

trimethoprim 200 mg TID [6 days] metformin 500 mg TID [10 days] 37 -32 Yes; CLcr↓20%;

SCr ↑23%

(Grun et al.,

2013)

trimethoprim 200 mg TID[5 days] metformin 850 mg QD [2 doses] 29.5 -26.4 Yes; CLcr↓16.9

%

(Muller et al.,

2015)




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vandetanib 100 mg QD [3 21-day-

cycles]

cisplatin 75 mg/m2 SD [3 21-

day-cycles]

32.7 - - (Blackhall et

al., 2010)

vandetanib 800 mg SD metformin 1000 mg SD 73.3 -52 Yes; SCr

↑ 8-29%, (Johansson

et al., 2014)

a: a time-dependent variation of SCr was observed. -: Data are not reported or available. NS: Not significant statistically.




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PEPT1/2

OAT4

MATE1/2K

OCTN1/2

OCT2

OCT3

OAT3

OAT2

OAT1

Blood Urine

OATP4C1

Figure 1

URAT1

MRP2/4

MDR1

BCRP


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ADP

ATP

Creatine

Guanidino-aceticacid

Creatinine

PhosphocreatineCreatine kinase

ADP ATP

HN

HN

O

CH3

N

NH2

H2N=C-NCH2CO2-

+

CH3

Creatinine

Creatine

ADP

ATP

Creatine

GAMTGAMT

Blood

Urine

SLC6A8

Arginine+

Glycine

AGATAGAT

Creatinine

Figure 2


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Glomerular filtration

Tubular secretion

Blood flow

Afferet arteriole

Glomerularcapsule

Glomerulus

Glomerularfiltrate

Efferent arteriole

Peritubularcapillary

Secretion

Cre

atin

ine

Renal tubule

Bloodflow

OAT2 ?

MATE1

MATE2K

OCT2

OCT3

OAT2

BloodUrine

Figure 3


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the complexities of interpreting reversible elevated serum...

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